Use Principal Components to form a smaller number of uncorrelated variables. The goal of principal components analysis is to explain the maximum amount of variance with the fewest number of principal components.

For example:

·    You record information on 10 socioeconomic variables and you want to reduce the variables into a smaller number of components to more easily analyze the data.

·    You want to analyze the customer responses to several attributes of a new product in order to form a smaller number of uncorrelated variables that are easier to interpret.

Principal components analysis is commonly used as one step in a series of analyses. For example, you can use Principal Components to reduce your data and avoid multicollinearity or when you have too many predictors relative to the number of observations.

A principal components analysis often uncovers unsuspected relationships, allowing you to interpret the data in a new way.

You can perform principal components analysis when you have one sample and several variables are measured on each sampling unit.

A bank requires eight pieces of information when applying for a loan: income, education level, age, length of time at current residence, length of time with current employer, savings, debt, and number of credit cards. A bank administrator wants to analyze this information for reporting purposes.

Data: LoanApplicant.MTW (available in the Sample Data folder).